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Representation and classification of Electroencephalography (EEG) brainsignals are critical processes for their analysis in cognitive tasks.Particularly, extraction of discriminative features from raw EEG signals,without any pre-processing, is a challenging task. Motivated by nuclear norm,we observed that there is a significant difference between the variances of EEGsignals captured from the same brain region when a subject performs differenttasks. This observation lead us to use singular value decomposition forcomputing dominant variances of EEG signals captured from a certain brainregion while performing a certain task and use them as features (nuclearfeatures). A simple and efficient class means based minimum distance classifier(CMMDC) is enough to predict brain states. This approach results in the featurespace of significantly small dimension and gives equally good classificationresults on clean as well as raw data. We validated the effectiveness androbustness of the technique using four datasets of different tasks: fluidintelligence clean data (FICD), fluid intelligence raw data (FIRD), memoryrecall task (MRT), and eyes open / eyes closed task (EOEC). For each task, weanalyzed EEG signals over six (06) different brain regions with 8, 16, 20, 18,18 and 100 electrodes. The nuclear features from frontal brain region gave the100% prediction accuracy. The discriminant analysis of the nuclear features hasbeen conducted using intra-class and inter-class variations. Comparisons withthe state-of-the-art techniques showed the superiority of the proposed system.

An Efficient Intelligent System for the Classification of  Electroencephalography (EEG) Brain Signals using Nuclear Features for Human  Cognitive Tasks